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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering
Rudi Ardiansyah
Program Studi Ilmu dan Teknologi Pangan Fakultas Pertanian USU Medan
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Pengendalian Persediaan Bahan Pendukung Pemasangan Batu Alam dengan Mempertimbangkan Klasifikasi FSN dan EOQ Ardiansyah, Rudi; Yuliawati, Evi
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 7, No 1 (2025): Jurnal SENOPATI Vol 7, No 1
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2025.v7i1.6225

Abstract

CV. XYZ is a company that operates in the interior sector, especially natural stone design and installation services. In the development of production activities there are shortages or excess stocks of supporting materials. The aim of this research is to help CV. XYZ identifies the causes of uncontrolled stock of supporting materials and prevents potential losses due to shortages or excess stock of materials. To determine this potential, the FSN method is used to classify supporting materials. then proceed with calculating using the EOQ method to find out the company's optimal order quantity. Then use Monte Carlo simulation to forecast needs for the following year. Where the results of these calculations will be used as suggestions for improvements to reduce the potential for excess and shortage of stock in the coming year. From the results of the analysis, potential shortages and excess stock arise due to the absence of special calculations and classifications which cause inventory to be ineffective
Klasifikasi serangan DDoS dengan metode random forest dan teknik class weight pada dataset CICDDoS2019 Mualfah, Desti; Ardiansyah, Rudi; Gunawan, Rahmad
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10731

Abstract

The rapid advancement of information technology has significantly influenced various aspects of life, including an increasing reliance on network-based services. However, this dependence has also led to the emergence of more complex cybersecurity threats, one of the most prominent being Distributed Denial of Service (DDoS) attacks. These attacks can disrupt service availability by overwhelming target systems with excessive traffic. A major challenge in detecting DDoS attacks lies in the wide variety of attack patterns and the class imbalance that commonly occurs in network traffic datasets. To address these issues, a machine learning–based approach capable of handling complex attack behaviors while compensating for imbalanced data distribution is required. One potential solution is the use of the Random Forest algorithm with class-weight techniques, applied to the CICDDoS2019 dataset. The research procedure includes data collection and exploration, preprocessing steps such as handling missing and infinite values, encoding categorical attributes, and feature normalization. The dataset is then divided into training and testing subsets before being processed by the Random Forest model. Model evaluation is conducted using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. Experimental results show that incorporating class weight significantly improves model performance, achieving an accuracy of 99.98%, precision of 99.98%, recall of 99.97%, and an F1-score of 99.97%. These findings demonstrate that the proposed approach is highly effective for accurately detecting and classifying DDoS attacks.
Co-Authors Evi Yuliawati Gunawan, Rahmad Mualfah, Desti